The acquisition of perceptual-motor skills and their adaptation to changing task demands is fundamental to everyday life. Loss of skill and adaptability is detrimental to functioning and is present in many neurological diseases of the sensorimotor system. Hence, further insights into these processes and their rehabilitation is of utmost significance. To elucidate the processes underlying acquisition, adaptation, and control of movements the proposed research tests the hypothesis that the central nervous system is exquisitely sensitive to its own variability and not only reduces unwanted intrinsic noise but has also developed strategies that accommodate and even utilize this noise. This hypothesis rests on two assumptions: first, the sensorimotor system has intrinsic neuromotor noise arising from complex hierarchical processes; second, behavioral tasks are typically redundant and afford many different ways to achieve equivalent task outcomes. Hence, the brain seeks solutions with stability that are robust with respect to its noise. Work of the previous funding cycle established that there are three conceptually distinct routes to achieve task stability: Tolerance: During practice humans explore and traverse the space of solutions in order to find those strategies that are tolerant to error and noise. Covariation: Task redundancy offers solutions where covariation among relevant variables achieves the same result in task performance while allowing variation in individual variables. Noise: When necessary, the amplitude of the random components can be reduced. This three-pronged TCN-distinction presents the quantitative framework to evaluate the hypothesis that in acquiring skilled behavior the central nervous system develops smart solutions that reduce, accommodate, and utilize the inevitable neuromotor noise. Twelve new experiments test this hypothesis and take findings as the platform to design novel intervention techniques. The research is organized into three aims: Experiments under Aim 1 focus on Tolerance and test whether the system seeks solutions that best accommodate for the individual's variability. Conversely, we also test whether manipulating the individual's variability can accelerate adaptation to tolerant solutions. Experiments under Aim 2 examine how Covariation of variables is achieved such that intrinsic noise has minimal effect on the task result. Augmented information is administered to investigate whether the acquisition of such trajectories can be facilitated. Experiments under Aim 3 examine whether intrinsic neuromotor Noise can be reduced by adding extrinsic noise and manipulating error information. The proposed research will be conducted on two tasks in parallel: Skittles, a target-oriented discrete throwing action predominantly under feedforward control, and Ball Bouncing, a continuous perceptually-guided skill of rhythmically hitting a ball. By performing equivalent experimental manipulations to both tasks, we test the generality of our hypothesis that the nervous system accommodates and utilizes intrinsic neuromotor noise in skilled behavior. Results from this quantitative TCN-approach will shed light on the control and acquisition of movement skills in ways that have not been addressed in any other extant research. Importantly, we also make the much-desired transition from new basic insights directly to intervention techniques. We propose three types of interventions that specifically aim to optimize task tolerance, maximize covariation, and reduce noise. We thereby establish the necessary bridge from theoretical concepts to practical techniques that will be applicable to a variety of neurological deficits. While the research proposed here is focused on healthy humans, complementary work is currently under way in close collaboration with Dr. Terence Sanger at Stanford University Medical Center that tests these concepts in children with dyskinetic cerebral palsy. The proposed work on healthy humans is an international collaboration with Dr. Hermann Mller at the University of Giessen, Germany, and Dr. Tjeerd Dijkstra at the University of Nijmegen, Netherlands, and will involve student exchanges.